A Pilot Study on the Use of Generative Adversarial Networks for Data Augmentation of Time Series

Autor:	Nicolas Morizet, Matteo Rizzato, David Grimbert, George Luta
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	data augmentation deep learning generative adversarial networks time series Electronic computers. Computer science QA75.5-76.95
Zdroj:	AI, Vol 3, Iss 4, Pp 789-795 (2022)
Druh dokumentu:	article
ISSN:	2673-2688
DOI:	10.3390/ai3040047
Popis:	Data augmentation is needed to use Deep Learning methods for the typically small time series datasets. There is limited literature on the evaluation of the performance of the use of Generative Adversarial Networks for time series data augmentation. We describe and discuss the results of a pilot study that extends a recent evaluation study of two families of data augmentation methods for time series (i.e., transformation-based methods and pattern-mixing methods), and provide recommendations for future work in this important area of research.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/bb14c0e7fdc344838e9b3d3ae112c6d8 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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